Jobs at TWG Global

Lead the design and deployment of machine learning, quantitative financial, recommendation, personalization, and generative AI solutions. Architect scalable cloud data pipelines, backend systems, APIs, and ML platforms using Java, Spring Boot, Kafka, SQL, Google Cloud, and LLM technologies. Collaborate with research, engineering, product, and business teams to improve search, discovery, feed recommendations, and financial analytics while shaping AI/ML roadmaps and reference architectures.

Own data strategy for a business unit, ensuring data readiness for AI initiatives. Assess data quality, accessibility, lineage, and suitability; partner with engineering teams on data preparation, transformation, ontology design, and knowledge-base construction. Define data roadmaps, integration priorities, governance frameworks, and quality standards for AI and agentic workflows. Translate strategy and technical trade-offs for executives and cross-functional teams.

Design and implement enterprise security architecture aligned with NIST, ISO 27001, and CIS standards. Build security data aggregation, normalization, and automation pipelines using VBScript, Python, and PowerShell. Lead tooling rationalization initiatives, consolidate security telemetry, reduce operational costs, and establish enterprise monitoring and incident response programs with detection, escalation, and investigation procedures.

Own the vision, roadmap, and lifecycle of AI-powered products for consulting and advisory workflows. Translate practitioner needs into scalable solutions involving document intelligence, forecasting, anomaly detection, conversational AI, and generative content. Partner with consultants, data scientists, engineers, designers, and business teams to drive discovery, delivery, adoption, metrics, responsible AI, budgets, and measurable business outcomes. The role is onsite in New York City.

Assist in developing and deploying AI/ML solutions, including agentic frameworks, RAG pipelines, LLM applications, personalization systems, and cloud-based ML platforms. Support model optimization, data workflows, ontology modeling, and Palantir Foundry/AIP implementations. Collaborate with research, engineering, product, and business teams to integrate generative AI into products, improve search and recommendations, and maintain secure, scalable reference architectures.

Supports quantitative research and data science projects involving financial, insurance, and alternative datasets. Responsibilities include data preparation, exploratory analysis, statistical and machine learning modeling, algorithm implementation in Python, forecasting, optimization, conversational AI, model validation, documentation, and presenting findings. The intern collaborates with senior researchers while applying responsible AI, transparency, safety, and regulatory standards.

Support the development, testing, evaluation, and productionization of AI/ML models and applications, including LLMs, agentic systems, document AI, forecasting, and optimization. Collaborate with engineering, product, business, and client stakeholders to translate requirements into technical work, establish performance metrics, maintain governance documentation, and monitor production systems for accuracy, cost, latency, and business impact.

Build and evaluate production AI systems across predictive modeling, forecasting, optimization, reinforcement learning, and LLM applications. Conduct exploratory data analysis, feature engineering, experimentation, and rigorous model evaluation. Collaborate with data scientists, ML engineers, business stakeholders, and operating teams to translate workflows into deployed solutions. Explore emerging AI tools, document work, report project performance, and support responsible AI governance.

Build and operate agentic AI products end to end, including LangGraph-based workflows, LLM evaluation systems, React interfaces, Java Spring Boot and Python services, REST APIs, iOS features, and distributed cloud infrastructure. Lead system design, testing, performance improvements, and engineering standards while mentoring junior engineers. Collaborate with product and design teams to deliver reliable multi-tenant applications and continuously improve agent quality through regression testing, trajectory scoring, and evaluation-driven development.

You will build agentic AI products across web, backend, and iOS, owning features end-to-end and mentoring junior teammates.

Lead the design and implementation of technical solutions, translating business requirements into actionable architectures while collaborating with clients and internal teams.

The Senior Software Engineer will design and build full-stack applications using React and Java, ensuring scalable systems, mentoring juniors, and collaborating with cross-functional teams.

Contribute to development, deployment, and monitoring of production ML models; perform data cleaning, analysis, and visualization; collaborate with cross-functional teams to deliver AI-driven business solutions and communicate insights to stakeholders.

Lead end-to-end design and deployment of production AI systems for marketing, sales, and operations. Build predictive models, experimentation and optimization frameworks, and LLM-powered applications (RAG, orchestration, structured extraction). Translate business workflows into production systems, partner with engineers to harden solutions, own client relationships, and mentor data scientists and engineers.